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Reach and grasp by people with tetraplegia using a neurally controlled robotic arm
Open accessAbstract
Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices. Able-bodied monkeys have used a neural interface system to control a robotic arm, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor 5 years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.
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“Additionally, it is apparent from previous work that access to sufficiently large patient populations for concrete and statistically significant conclusions may be difficult to obtain (1–5, 8, 11, 29, 39).”
From Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019) · cites this paper as [39]
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“The flexibility of ConvNets might also be a limitation in some brain‐signal decoding scenarios. For example if the user of a brain–computer interface should learn to adapt her or his brain signals to the decoding model, a simpler feature‐based model might yield better results.”
From Deep learning with convolutional neural networks for EEG decoding and visualization (2017) · cites this paper as Hochberg et al., 2012
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“Subcutaneous invasive electrophysiological signals can be derived through brain interfaces,[270–272] using electrocorticography (ECoG)[273,274] on the cortical surface as well as local field potentials (LFP)[275,276] extracted from electrodes inserted into the cortex.”
From Flexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics (2022) · cites this paper as [275]
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“Prior BCI studies have shown that the motor intention for gross motor skills, such as reaching, grasping or moving a computer cursor, remains neurally encoded in motor cortex after paralysis1–7.”
From High-performance brain-to-text communication via handwriting (2021) · cites this paper as [1]
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“Currently, all reported BMI experiments with human patients [26, 35, 57, 58, 150] require the subjects tethered percutaneously to a bench-top computer for neural signal acquisition and decoding.”
From Implantable neurotechnologies: a review of integrated circuit neural amplifiers (2016) · cites this paper as [58]
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“The results presented here demonstrate that CP control provides a unique opportunity for the complex control of a virtual cursor and robotic device (14, 39), without requiring discretized, prolonged task sequences (46) that can make even simple task completion long and frustrating.”
From Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019) · cites this paper as [39]
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“Highcount microelectrode arrays have proven useful in basic physiology research [8], and for clinical applications such as the restoration of function in people with nervous system injury [9] or stroke [10].”
From A new high-density (25 electrodes/mm2) penetrating microelectrode array for recording and stimulating sub-millimeter neuroanatomical structures (2013) · cites this paper as [10]
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“Recent penetrating neural interface applications are requiring a greater number of electrodes (>60) to achieve complex neuromodulation [9,10,16] or neurophysiological [8] goals.”
From A new high-density (25 electrodes/mm2) penetrating microelectrode array for recording and stimulating sub-millimeter neuroanatomical structures (2013) · cites this paper as [10]
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Bioactive Neuroelectronic Interfaces
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A collaborative BCI approach to autonomous control of a prosthetic limb system
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Autonomy Infused Teleoperation with Application to BCI Manipulation
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Cortical Decoding of Individual Finger Group Motions Using ReFIT Kalman Filter
Vaskov, A. K., Irwin, Z. T., Nason, S. R., Vu, P. P., Nu, C. S., Bullard, A. J., Hill, M., North, N., Patil, P. G., & Chestek, C. A. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00751 · Cited by 50
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Szymanski, L. J., Kellis, S., Liu, C. Y., Jones, K. T., Andersen, R. A., Commins, D., Lee, B., McCreery, D. B., & Miller, C. A. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac127e · Cited by 48
CLoSES: A platform for closed-loop intracranial stimulation in humans
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Self-citationIn vivo spatiotemporal dynamics of astrocyte reactivity following neural electrode implantation
Savya, S. P., Li, F., Lam, S., Wellman, S. M., Stieger, K. C., Chen, K., Eles, J. R., & Kozai, T. D. Y. (2022) · Biomaterials · doi:10.1016/j.biomaterials.2022.121784 · Cited by 46
Modified Neuropixels probes for recording human neurophysiology in the operating room
Coughlin, B. F., Muñoz, W., Kfir, Y., Young, M. J., Meszéna, D., Jamali, M., Caprara, I., Hardstone, R., Khanna, A. R., Mustroph, M. L., et al. (2023) · Nature Protocols · doi:10.1038/s41596-023-00871-2 · Cited by 47
Self-citationThe impact of mind-body awareness training on the early learning of a brain-computer interface
Cassady, K. E., You, A. K., Doud, A., & He, B. (2014) · TECHNOLOGY · doi:10.1142/s233954781450023x · Cited by 49
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Matsushita, K., Hirata, M., Suzuki, T., Ando, H., Yoshida, T., Ota, Y., Satō, F., Morris, S., Sugata, H., Goto, T., et al. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00511 · Cited by 48
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EDAN: An EMG-controlled Daily Assistant to Help People With Physical Disabilities
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Self-citationSystems Neuroengineering: Understanding and Interacting with the Brain
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Gels, jets, mosquitoes, and magnets: a review of implantation strategies for soft neural probes
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Jeong, J.-H., Cho, J.-H., Lee, B.-H., & Lee, S. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3211694 · Cited by 46
A comparison of neuroinflammation to implanted microelectrodes in rat and mouse models
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High data rate ultrasonic communications for wireless intra-body networks
Demirors, E., Giovanni, A., Santagati, G. E., & Melodia, T. (2016) · IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN) · doi:10.1109/lanman.2016.7548843 · Cited by 47
Physiological properties of brain-machine interface input signals
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Belkacem, A. N., Nishio, S., Suzuki, T., Ishiguro, H., & Hirata, M. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2837003 · Cited by 45
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Decoding movement kinematics from EEG using an interpretable convolutional neural network
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Thought-Controlled Nanoscale Robots in a Living Host
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Metadata
- Authors
- Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., Haddadin, S., Liu, J., Cash, S. S., van der Smagt, P., & Donoghue, J. P.
- Affiliations
- Brown University (US)
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
- Harvard University (US)
- Corresponding author
- Hochberg, Leigh R.
- Donoghue, John P.
- ORCID
- Venue
- Nature
- Published
- 2012-05
- Volume
- 485
- Issue
- 7398
- Pages
- 372-375
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3640850
- OpenAlex
- W2087704839
- Cited by
- 2,719
- References
- 41
- Citations by year
- 2026: 123 · 2025: 170 · 2024: 176 · 2023: 196 · 2022: 166 · 2021: 199 · 2020: 227 · 2019: 206 · 2018: 201 · 2017: 185 · 2016: 203 · 2015: 189 · 2014: 240 · 2013: 184 · 2012: 50
- FWCI
- 107.19
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- elib (German Aerospace Center) (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- mediaTUM (Technical University of Munich) (submittedVersion, other-oa)
- Funders
- U.S. Department of Veterans Affairs (A6779-I, B6453-R, B6310N)
- Doris Duke Charitable Foundation
- Craig H. Neilsen Foundation
- Office of Research and Development (A6779I, B6453R)
- European Commission (248587)
- National Institutes of Health (N01-HD-5-3403, n01hd53403-6-0-1, N01HD10018)
- Defense Advanced Research Projects Agency
- Rehabilitation Research and Development Service (B6453R, A6779I)
- Advanced Research Projects Agency
- National Institute on Deafness and Other Communication Disorders (R01DC009899)
- National Institute of Neurological Disorders and Stroke (NS-25074, R37NS025074, R01NS025074, R56 NS025074)
- National Institute of Biomedical Imaging and Bioengineering (R01EB007401)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (RC1HD063931, HHSN275201100018C, N01HD10018, N01 HD053403)
- National Center for Medical Rehabilitation Research (N01HD53403, N01HD10018)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- tetraplegia, brain-computer interface, robot manipulators, neural interfaces, motor cortex, reach and grasp, upper limb paralysis, microelectrode arrays, long-term implantation, assistive devices, neural decoding, stroke, spinal cord injury, amyotrophic lateral sclerosis
- MeSH terms
- Aged, Arm, Calibration, Drinking, Female, Hand, Humans, Male, Man-Machine Systems, Microelectrodes, Middle Aged, Motor Cortex, Movement, Psychomotor Performance, Quadriplegia, Robotics, Time Factors, Hand Strength
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